World Happiness Report 2024

Figure 2.1: Distribution of Contributing Factors to Life Evaluations

MakeoverMonday
Data Visualization
R Programming
2025
A visual exploration of happiness factors across nations, transforming the World Happiness Report’s stacked bar chart into an intuitive beeswarm plot that highlights the distribution and relationships between different contributors to national happiness.
Author

Steven Ponce

Published

January 30, 2025

Original

The original visualization comes from the World Happiness Report 2024, Figure 2.1, showing country rankings by life evaluations in 2021-2023.

You can view the interactive visualization here on Tableau Public.

Original visualization from World Happiness Report 2024

Source: World Happiness Report 2024

Makeover

Figure 1: A beeswarm plot showing the distribution of different factors contributing to happiness scores across countries. Factors include Generosity, Low Corruption, Health, Freedom, Social Support, GDP, and overall Happiness Score. Each point represents a country, with Finland and Afghanistan often appearing as extremes. Values are color-coded from blue (low) to brown (high), with red diamond markers showing mean values for each factor.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder,      # Record Your Plot History 
    readxl,         # Read Excel Files
    ggbeeswarm,     # Categorical Scatter (Violin Point) Plots 
    scico,          # Colour Palettes Based on the Scientific Colour-Maps 
    ggrepel         # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
)
})

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
world_happiness_2024 <- read_excel(
  here::here('data/DataForFigure2.1 with sub bars 2024.xlsx')) |> 
  clean_names()

3. Examine the Data

Show code
glimpse(world_happiness_2024)
skim(world_happiness_2024)

4. Tidy Data

Show code
### |-  tidy data ----
happiness_long <- world_happiness_2024 |>
  select(
    # Rename columns
    country_name,
    "Happiness Score" = ladder_score,
    "GDP" = explained_by_log_gdp_per_capita,
    "Social Support" = explained_by_social_support,
    "Health" = explained_by_healthy_life_expectancy,
    "Freedom" = explained_by_freedom_to_make_life_choices,
    "Generosity" = explained_by_generosity,
    "Low Corruption" = explained_by_perceptions_of_corruption
  ) |>
  # Pivot longer
  pivot_longer(
    -country_name,
    names_to = "metric",
    values_to = "value"
  ) |>
  # add metric mea
  group_by(metric) |>
  mutate(
    metric_mean = mean(value, na.rm = TRUE)
  ) |>
  ungroup() |>
  # Factor reorder by mean
  mutate(
    metric = fct_reorder(metric, metric_mean, .desc = TRUE)
  )

# data labels df
label_data <- bind_rows(
  # Get minimum values
  happiness_long |>
    group_by(metric) |>
    slice_min(order_by = value, n = 1, with_ties = FALSE),
  
  # Get maximum values
  happiness_long |>
    group_by(metric) |>
    slice_max(order_by = value, n = 1, with_ties = FALSE)
  ) |>
  ungroup() |>
  distinct(metric, country_name, value)

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors()

### |-  titles and caption ----
title_text <- str_glue("World Happiness Report 2024")
subtitle_text <- str_glue("Figure 2.1: Distribution of Contributing Factors to Life Evaluations")

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 05,
    source_text = "World Happiness Report Data Dashboard | The World Happiness Report"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
      # Legend
      legend.position = c(0.8, 1.1),    
      legend.justification = c(0.5, 1),
      legend.margin = margin(b = 5),
      legend.title = element_text(size = rel(0.7)),
      legend.text = element_text(size = rel(0.6)),
      legend.direction = "horizontal",
      
      # Axis formatting
      axis.title = element_text(color = colors$text, size = rel(1), face = "bold"),
      axis.text.y = element_text(color = colors$text, size = rel(0.95), face = "bold"),
      
      # Grid customization
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank(),
    
      # Plot margins 
      plot.margin = margin(t = 20, r = 20, b = 20, l = 20)
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot  ----
p <- ggplot(happiness_long, aes(x = metric, y = value)) +
  # Geom
  geom_hline(
    yintercept = seq(0, 8, by = 2),
    color = "gray90",
    linewidth = 0.5
  ) +
  geom_quasirandom(
    aes(color = value),
    width = 0.4,
    size = 2.5,
    alpha = 0.7,
    shape = 21,
    stroke = 0.5
  ) +
  geom_text_repel(
    data = label_data,
    aes(label = country_name),
    size = 3,
    fontface = "plain",
    max.overlaps = Inf,
    box.padding = 0.5,
    point.padding = 0.3,
    segment.color = "gray40",
    segment.size = 0.3,
    min.segment.length = 0,
    seed = 123,           
    direction = "both",   
    force = 1,           
    force_pull = 0.5     
  ) +
  geom_point(                     # Mean indicators
    data = distinct(happiness_long, metric, metric_mean),
    aes(y = metric_mean),
    color = "red",
    fill = "white",
    size = 3,
    shape = 23
  ) +
  # Scales
  scale_x_discrete() +
  scale_y_continuous(
    expand = expansion(mult = c(0.35, 0)),
    breaks = seq(0, 8, by = 2),
    limits = c(0, 9)) +
  scale_color_scico(
    palette = "roma",
    direction = -1,
    guide = guide_colorbar(
      title.position = "top",
      barwidth = 10,
      barheight = 1,
      ticks.linewidth = 1,
      title.hjust = 0.5
    )
  ) +
  coord_flip(clip = 'off') +
  # Labs
  labs(
    x = NULL,
    y = "Value",
    color = "Value",
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size   = rel(2),
      family = fonts$title,
      face   = "bold",
      color  = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size   = rel(0.95),
      family = fonts$subtitle,
      color  = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size   = rel(0.6),
      family = fonts$caption,
      color  = colors$caption,
      hjust  = 0.5,
      margin = margin(t = 10)
    )
  )

7. Save

Show code
### |-  plot image ----  
save_plot (
  p, type = "makeovermonday", 
  year = 2025, week = 05, width = 10, height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] ggrepel_0.9.6    scico_1.5.0      ggbeeswarm_0.7.2 readxl_1.4.3    
 [5] camcorder_0.1.0  here_1.0.1       glue_1.8.0       scales_1.3.0    
 [9] skimr_2.1.5      janitor_2.2.0    showtext_0.9-7   showtextdb_3.0  
[13] sysfonts_0.8.9   ggtext_0.1.2     lubridate_1.9.3  forcats_1.0.0   
[17] stringr_1.5.1    dplyr_1.1.4      purrr_1.0.2      readr_2.1.5     
[21] tidyr_1.3.1      tibble_3.2.1     ggplot2_3.5.1    tidyverse_2.0.0 

loaded via a namespace (and not attached):
 [1] beeswarm_0.4.0    gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4
 [5] tzdb_0.4.0        vctrs_0.6.5       tools_4.4.0       generics_0.1.3   
 [9] curl_6.0.0        gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  vipor_0.4.7       htmltools_0.5.8.1 yaml_2.3.10      
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     labeling_0.4.3    rsvg_2.6.1       
[33] rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1 
[37] cli_3.6.3         magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4       
[41] withr_3.0.2       timechange_0.3.0  rmarkdown_2.29    cellranger_1.1.0 
[45] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[49] markdown_1.13     rlang_1.1.4       gridtext_0.1.5    Rcpp_1.0.13-1    
[53] xml2_1.3.6        renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1
[57] jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_05.qmd.

For the full repository, click here.

10. References

  1. Article:
  2. Data:
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